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Pitch and fargan model updates

Removing one of the 2d conv layers for pitch estimation reduces
complexity without noticeable degradation. FARGAN model has more
adversarial training.
Also, no need for the double precision in the low-pass filter.

--- a/autogen.sh

+++ b/autogen.sh

@@ -9,7 +9,7 @@

 srcdir=`dirname $0`

 test -n "$srcdir" && cd "$srcdir"

-dnn/download_model.sh 290be25

+dnn/download_model.sh c99054d

 echo "Updating build configuration files, please wait...."

--- a/dnn/lpcnet_enc.c

+++ b/dnn/lpcnet_enc.c

@@ -81,8 +81,8 @@

     float xi, yi;

     xi = x[i];

     yi = x[i] + mem[0];

-    mem[0] = mem[1] + (b[0]*(double)xi - a[0]*(double)yi);

-    mem[1] = (b[1]*(double)xi - a[1]*(double)yi);

+    mem[0] = mem[1] + (b[0]*xi - a[0]*yi);

+    mem[1] = (b[1]*xi - a[1]*yi);

     y[i] = yi;

   }

 }

--- a/dnn/pitchdnn.c

+++ b/dnn/pitchdnn.c

@@ -33,8 +33,7 @@

   /* xcorr*/

   OPUS_COPY(&conv1_tmp1[1], xcorr_features, NB_XCORR_FEATURES);

   compute_conv2d(&model->conv2d_1, &conv1_tmp2[1], st->xcorr_mem1, conv1_tmp1, NB_XCORR_FEATURES, NB_XCORR_FEATURES+2, ACTIVATION_TANH);

-  compute_conv2d(&model->conv2d_2, &conv1_tmp1[1], st->xcorr_mem2, conv1_tmp2, NB_XCORR_FEATURES, NB_XCORR_FEATURES+2, ACTIVATION_TANH);

-  compute_conv2d(&model->conv2d_3, downsampler_in, st->xcorr_mem3, conv1_tmp1, NB_XCORR_FEATURES, NB_XCORR_FEATURES, ACTIVATION_TANH);

+  compute_conv2d(&model->conv2d_2, downsampler_in, st->xcorr_mem2, conv1_tmp2, NB_XCORR_FEATURES, NB_XCORR_FEATURES, ACTIVATION_TANH);

   compute_generic_dense(&model->dense_downsampler, downsampler_out, downsampler_in, ACTIVATION_TANH);

   compute_generic_gru(&model->gru_1_input, &model->gru_1_recurrent, st->gru_state, downsampler_out);

--- a/dnn/torch/neural-pitch/export_neuralpitch_weights.py

+++ b/dnn/torch/neural-pitch/export_neuralpitch_weights.py

@@ -73,8 +73,7 @@

     conv_layers = [

         ('conv.1', "conv2d_1"),

-        ('conv.4', "conv2d_2"),

-        ('conv.7', "conv2d_3")

+        ('conv.4', "conv2d_2")

     ]

--- a/dnn/torch/neural-pitch/models.py

+++ b/dnn/torch/neural-pitch/models.py

@@ -86,13 +86,10 @@

         self.conv = torch.nn.Sequential(

             torch.nn.ZeroPad2d((2,0,1,1)),

-            torch.nn.Conv2d(1, 8, 3, bias=True),

+            torch.nn.Conv2d(1, 4, 3, bias=True),

             self.activation,

             torch.nn.ZeroPad2d((2,0,1,1)),

-            torch.nn.Conv2d(8, 8, 3, bias=True),

-            self.activation,

-            torch.nn.ZeroPad2d((2,0,1,1)),

-            torch.nn.Conv2d(8, 1, 3, bias=True),

+            torch.nn.Conv2d(4, 1, 3, bias=True),

             self.activation,

         )

-- 

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